{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af762997",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"3d0fdaaa02aa44b1825840e20199ed1e\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_3d0fdaaa02aa44b1825840e20199ed1e = echarts.init(\n",
       "                    document.getElementById('3d0fdaaa02aa44b1825840e20199ed1e'), 'white', {renderer: 'canvas'});\n",
       "                var option_3d0fdaaa02aa44b1825840e20199ed1e = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u5546\\u5bb6A\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                5,\n",
       "                20,\n",
       "                36,\n",
       "                10,\n",
       "                75,\n",
       "                90\n",
       "            ],\n",
       "            \"realtimeSort\": false,\n",
       "            \"showBackground\": false,\n",
       "            \"stackStrategy\": \"samesign\",\n",
       "            \"cursor\": \"pointer\",\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5546\\u5bb6A\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u5546\\u5bb6A\": true\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"enterable\": false,\n",
       "        \"confine\": false,\n",
       "        \"appendToBody\": false,\n",
       "        \"transitionDuration\": 0.4,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5,\n",
       "        \"order\": \"seriesAsc\"\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u886c\\u886b\",\n",
       "                \"\\u7f8a\\u6bdb\\u886b\",\n",
       "                \"\\u96ea\\u7eba\\u886b\",\n",
       "                \"\\u88e4\\u5b50\",\n",
       "                \"\\u9ad8\\u8ddf\\u978b\",\n",
       "                \"\\u889c\\u5b50\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_3d0fdaaa02aa44b1825840e20199ed1e.setOption(option_3d0fdaaa02aa44b1825840e20199ed1e);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x2067aecc160>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Bar\n",
    "\n",
    "bar = Bar()\n",
    "bar.add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
    "bar.add_yaxis(\"商家A\", [5, 20, 36, 10, 75, 90])\n",
    "# render 会生成本地 HTML 文件，默认会在当前目录生成 render.html 文件\n",
    "# 也可以传入路径参数，如 bar.render(\"mycharts.html\")\n",
    "bar.render_notebook ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5843eaf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a13252f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('tsne_vis_3d.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4051782a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.09544"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df['Z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51473000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 X\n",
      "1 Y\n",
      "2 Z\n",
      "3 item\n",
      "4 pagerank\n",
      "5 chosen\n"
     ]
    }
   ],
   "source": [
    "for index, item in enumerate(df):\n",
    "    print(index, item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3f0d6085",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'aiohttp'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16540\\2166206694.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0masyncio\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0maiohttp\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTCPConnector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mClientSession\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyecharts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopts\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpyecharts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharts\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mScatter3D\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'aiohttp'"
     ]
    }
   ],
   "source": [
    "import asyncio\n",
    "# from aiohttp import TCPConnector, ClientSession\n",
    "\n",
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Scatter3D\n",
    "\n",
    "\"\"\"\n",
    "Gallery 使用 pyecharts 1.1.0\n",
    "参考地址: https://echarts.apache.org/examples/editor.html?c=scatter3d&gl=1&theme=dark\n",
    "\n",
    "目前无法实现的功能:\n",
    "\n",
    "1、暂时无法对 Grid3D 设置 轴线和轴坐标的 style (非白色背景下有问题)\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "# async def get_json_data(url: str) -> dict:\n",
    "#     async with ClientSession(connector=TCPConnector(ssl=False)) as session:\n",
    "#         async with session.get(url=url) as response:\n",
    "#             return await response.json()\n",
    "\n",
    "\n",
    "# 获取官方的数据\n",
    "# data = asyncio.run(\n",
    "#     get_json_data(\n",
    "#         url=\"https://echarts.apache.org/examples/data/asset/data/nutrients.json\"\n",
    "#     )\n",
    "# )\n",
    "\n",
    "# 列名映射\n",
    "field_indices = {\n",
    "    \"calcium\": 3,\n",
    "    \"calories\": 12,\n",
    "    \"carbohydrate\": 8,\n",
    "    \"fat\": 10,\n",
    "    \"fiber\": 5,\n",
    "    \"group\": 1,\n",
    "    \"id\": 16,\n",
    "    \"monounsat\": 14,\n",
    "    \"name\": 0,\n",
    "    \"polyunsat\": 15,\n",
    "    \"potassium\": 7,\n",
    "    \"protein\": 2,\n",
    "    \"saturated\": 13,\n",
    "    \"sodium\": 4,\n",
    "    \"sugars\": 9,\n",
    "    \"vitaminc\": 6,\n",
    "    \"water\": 11,\n",
    "}\n",
    "\n",
    "# 配置 config\n",
    "config_xAxis3D = \"protein\"\n",
    "config_yAxis3D = \"fiber\"\n",
    "config_zAxis3D = \"sodium\"\n",
    "config_color = \"fiber\"\n",
    "config_symbolSize = \"vitaminc\"\n",
    "\n",
    "# 构造数据\n",
    "data = [\n",
    "    [\n",
    "        item[field_indices[config_xAxis3D]],\n",
    "        item[field_indices[config_yAxis3D]],\n",
    "        item[field_indices[config_zAxis3D]],\n",
    "        item[field_indices[config_color]],\n",
    "        item[field_indices[config_symbolSize]],\n",
    "        index,\n",
    "    ]\n",
    "    for index, item in enumerate(data)\n",
    "]\n",
    "\n",
    "(\n",
    "    Scatter3D()  # bg_color=\"black\"\n",
    "    .add(\n",
    "        series_name=\"\",\n",
    "        data=data,\n",
    "        xaxis3d_opts=opts.Axis3DOpts(\n",
    "            name=config_xAxis3D,\n",
    "            type_=\"value\",\n",
    "            # textstyle_opts=opts.TextStyleOpts(color=\"#fff\"),\n",
    "        ),\n",
    "        yaxis3d_opts=opts.Axis3DOpts(\n",
    "            name=config_yAxis3D,\n",
    "            type_=\"value\",\n",
    "            # textstyle_opts=opts.TextStyleOpts(color=\"#fff\"),\n",
    "        ),\n",
    "        zaxis3d_opts=opts.Axis3DOpts(\n",
    "            name=config_zAxis3D,\n",
    "            type_=\"value\",\n",
    "            # textstyle_opts=opts.TextStyleOpts(color=\"#fff\"),\n",
    "        ),\n",
    "        grid3d_opts=opts.Grid3DOpts(width=100, height=100, depth=100),\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        visualmap_opts=[\n",
    "            opts.VisualMapOpts(\n",
    "                type_=\"color\",\n",
    "                is_calculable=True,\n",
    "                dimension=3,\n",
    "                pos_top=\"10\",\n",
    "                max_=79 / 2,\n",
    "                range_color=[\n",
    "                    \"#1710c0\",\n",
    "                    \"#0b9df0\",\n",
    "                    \"#00fea8\",\n",
    "                    \"#00ff0d\",\n",
    "                    \"#f5f811\",\n",
    "                    \"#f09a09\",\n",
    "                    \"#fe0300\",\n",
    "                ],\n",
    "            ),\n",
    "            opts.VisualMapOpts(\n",
    "                type_=\"size\",\n",
    "                is_calculable=True,\n",
    "                dimension=4,\n",
    "                pos_bottom=\"10\",\n",
    "                max_=2.4 / 2,\n",
    "                range_size=[10, 40],\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    "    .render(\"scatter3d.html\")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "46aeba32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Network data\\\\Networkx图机器学习工具包实战-20230117\\\\DeepWalk代码实战\\\\test2.html'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    " \n",
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Scatter3D\n",
    "from pyecharts.faker import Faker\n",
    " \n",
    "# 定义变量\n",
    "xdata = list(range(-50,50))\n",
    "ydata = list(range(-70,20))\n",
    "zdata = list(range(-50,50))\n",
    "data = [tuple(z) for z in zip(df['X'], df['Y'], df['Z'])]\n",
    " \n",
    "scatter3d = (\n",
    "    # 3D散点图\n",
    "    Scatter3D(\n",
    "        # 初始化配置项\n",
    "        init_opts=opts.InitOpts(\n",
    "            theme='white',  # 图表主题 white dark\n",
    "        )\n",
    "    )\n",
    "    # 数据配置\n",
    "    .add(\n",
    "        series_name='销售数量',  # 系列名称\n",
    "        data=data,  # 数值 格式为[(x,y,z),(x,y,z)]\n",
    "        xaxis3d_opts=opts.Axis3DOpts(data=[list(z) for z in zip(xdata)]),   # X轴数据项 格式为[名称1, 名称2]\n",
    "        yaxis3d_opts=opts.Axis3DOpts(data=[list(z) for z in zip(ydata)]),   # Y轴数据项 格式为[名称1, 名称2]\n",
    "    )\n",
    "    # !!!!全局配置项!!!!\n",
    "    .set_global_opts(\n",
    "        # 标题配置项\n",
    "        title_opts=opts.TitleOpts(\n",
    "            title=\"3D散点图\",  # 主标题\n",
    "        ),\n",
    "        # 视觉映射配置项\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            is_show=True,  # 是否显示视觉映射配置\n",
    "        ),\n",
    "    )\n",
    ")\n",
    "scatter3d.render(\"test2.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e5c486a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [tuple(z) for z in zip(df['X'], df['Y'], df['Z'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "85619a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>item</th>\n",
       "      <th>pagerank</th>\n",
       "      <th>chosen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-12.011850</td>\n",
       "      <td>-5.589834</td>\n",
       "      <td>-4.372107</td>\n",
       "      <td>graph theory</td>\n",
       "      <td>0.012455</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.114214</td>\n",
       "      <td>11.764197</td>\n",
       "      <td>-1.342994</td>\n",
       "      <td>collective intelligence</td>\n",
       "      <td>0.004321</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.038623</td>\n",
       "      <td>5.617672</td>\n",
       "      <td>-0.708329</td>\n",
       "      <td>cognitive science</td>\n",
       "      <td>0.003786</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-9.139861</td>\n",
       "      <td>-17.583952</td>\n",
       "      <td>-1.229296</td>\n",
       "      <td>low-density parity-check code</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.548052</td>\n",
       "      <td>-8.413167</td>\n",
       "      <td>-14.334480</td>\n",
       "      <td>propositional calculus</td>\n",
       "      <td>0.003182</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           X          Y          Z                           item  pagerank  \\\n",
       "0 -12.011850  -5.589834  -4.372107                   graph theory  0.012455   \n",
       "1  -0.114214  11.764197  -1.342994        collective intelligence  0.004321   \n",
       "2   5.038623   5.617672  -0.708329              cognitive science  0.003786   \n",
       "3  -9.139861 -17.583952  -1.229296  low-density parity-check code  0.003667   \n",
       "4  -2.548052  -8.413167 -14.334480         propositional calculus  0.003182   \n",
       "\n",
       "   chosen  \n",
       "0     1.0  \n",
       "1     1.0  \n",
       "2     1.0  \n",
       "3     1.0  \n",
       "4     1.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fa0aa69b",
   "metadata": {},
   "outputs": [
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       " (-8.439405, -3.588879, 18.800745),\n",
       " (18.470825, -4.929828, 7.3793745),\n",
       " (6.8453274, 11.413962, -6.2304835),\n",
       " (-6.179047, 18.013414, 1.798026),\n",
       " (8.106173, 19.840168, -0.91370225),\n",
       " (-0.008402524, -7.5575604, 14.622346),\n",
       " (7.1743326, -4.437315, -4.829876),\n",
       " (17.898958, -7.665462, 6.79869),\n",
       " (6.3369007, 16.048006, -5.1464663),\n",
       " (-9.833802, 12.459317, 3.539143),\n",
       " (-9.843986, 12.48961, 3.3050587),\n",
       " (16.459387, -5.370664, 2.5144515),\n",
       " (6.2105894, -0.8249632, 6.1682086),\n",
       " (11.784517, 12.945446, -2.6650674),\n",
       " (1.9325161, 4.1638875, -13.369333),\n",
       " (-15.561605, 19.329096, 4.111455),\n",
       " (9.978706, 0.98228705, -0.8709467),\n",
       " (-3.564186, -2.7689304, -3.5491514),\n",
       " (2.3525612, -12.338815, -13.571268),\n",
       " (-3.6737127, -4.445537, 8.811622),\n",
       " (-2.8982258, -2.3757315, -3.4157717),\n",
       " (-14.412781, -12.896148, -2.99455),\n",
       " (0.45034888, -15.487649, 0.46721235),\n",
       " (-18.936094, 2.5596373, -6.8800807),\n",
       " (-0.5003111, 15.713407, 4.8132114),\n",
       " (2.2404068, 17.35873, 4.3576612),\n",
       " (1.2323953, -14.966463, 6.553133),\n",
       " (-10.084435, 5.3621945, 16.302286),\n",
       " (-5.0290775, -9.270901, 1.285785),\n",
       " (5.7454743, -3.0025878, -2.6436787),\n",
       " (17.017542, 4.7914863, 5.505624),\n",
       " (-13.897096, 1.1658047, 2.7983692),\n",
       " (3.31699, -11.582827, 15.32266),\n",
       " (-10.383831, -1.0376272, 16.924816),\n",
       " (-13.480096, 14.513027, 1.1215035),\n",
       " (-7.8857455, -4.937042, -8.953917),\n",
       " ...]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  }
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